In this section, we will discuss how to train neural networks to obtain evidence for each view, which can then be used to obtain the corresponding masses \(\lbrace \mathcal {M}^v\rbrace _{v=1}^V\)  and \(\mathcal {M}\) . The neural networks can capture the evidence from input to induce a classification opinion [1]}, and the conventional neural-network-based classifier can be naturally transformed into the evidence-based classifier with minor changes. Specifically, the softmax layer of a conventional neural-network-based classifier is replaced with an activation function layer (i.e., RELU) to ensure that the network outputs non-negative values, which are considered as the evidence vector \(e\) . Accordingly, the parameters of the Dirichlet distribution can be obtained.
